Infections with resistant bacteria or resistance evolving during the course of treatment are major reasons antibiotic treatment fails. But this inherited resistance is not only reason treatment fails; patients remain ill for extensive periods or die due to infections with bacteria that are and remain fully susceptible to the antibiotics used for treatment. The goals of the proposed studies are to develop and evaluate antibiotic treatment protocols that are effective in rapidly clearing bacterial infections and, at the same time, minimize the likelihood of resistance evolving during the course of treatment. To achieve these goals, we will construct and analyze the properties of mathematical and computer simulation models that combine the pharmacodynamics of antibiotics and bacteria and the pharmacokinetics of the antibiotic treatment, with the population and evolutionary dynamics of bacteria in infected hosts. Using methicillin sensitive and resistant Staphylococcus aureus (MSSA and MSRA) and E. coli in invitro culture, we will estimate the parameters of these models and evaluate the validity of the assumptions behind their construction and test the predictions (hypotheses) generated from our analysis of their properties. Based on the results of these experiments, we will modify these models to make them more accurate and proposed the single and multi- drug treatment protocols to increase their efficacy in clearing bacterial infections and preventing the evolution of resistance. Of particular concern in these investigations are bacteria- and host-mediated processes that make genetically susceptible bacteria refractory to antibiotics. Included among these mechanisms of non-inherited resistance are subpopulations of non-growing bacteria (persistence) the physical structure of the infecting population (biofilms), the density of the infection, and physiological state of the bacteria (latent stages). PUBLIC HEALTH RELEVANCE: A theoretical and experimental study will be performed to improve the efficacy of antibiotic treatment and prevent the evolution of resistance. To achieve this end, we will use mathematical models, computer simulations and experiments with Staphylococcus aureus and E.coli. Particular consideration will be given to methicillin resistant S. aureus infections (MRSA).